deepGBLUP: joint deep learning networks and GBLUP framework for accurate genomic prediction of complex traits in Korean native cattle

Lee, Hyo-Jun; Lee, Jun Heon; Gondro, Cedric; Koh, Yeong Jun; Lee, Seung Hwan

doi:10.1186/s12711-023-00825-y

Genetics Selection Evolution

Table 4 Performance comparison of deepGBLUP with the other genomic prediction methods on the Korean native cattle dataset across different traits and training sizes

From: deepGBLUP: joint deep learning networks and GBLUP framework for accurate genomic prediction of complex traits in Korean native cattle

Train size	Method	CWT	BF	EMA	MS
9000	GBLUP	0.729 ± 0.015	0.647 ± 0.009	0.726 ± 0.017	0.670 ± 0.014
	DGBLUP	0.731 ± 0.016	0.639 ± 0.01	0.729 ± 0.017	0.668 ± 0.013
	EGBLUP	0.724 ± 0.016	0.641 ± 0.01	0.721 ± 0.019	0.664 ± 0.014
	BayesA	0.730 ± 0.015	0.658 ± 0.009	0.720 ± 0.016	0.667 ± 0.014
	BayesB	0.746 ± 0.015	0.667 ± 0.009	0.723 ± 0.019	0.670 ± 0.013
	BayesC	0.737 ± 0.015	0.662 ± 0.01	0.726 ± 0.018	0.668 ± 0.014
	deepGBLUP	0.752 ± 0.016	0.673 ± 0.009	0.746 ± 0.017	0.672 ± 0.012
5000	GBLUP	0.682 ± 0.018	0.581 ± 0.009	0.679 ± 0.018	0.609 ± 0.012
	DGBLUP	0.684 ± 0.018	0.576 ± 0.009	0.683 ± 0.019	0.610 ± 0.012
	EGBLUP	0.678 ± 0.017	0.578 ± 0.01	0.676 ± 0.019	0.606 ± 0.013
	BayesA	0.678 ± 0.018	0.581 ± 0.008	0.664 ± 0.017	0.602 ± 0.012
	BayesB	0.697 ± 0.017	0.593 ± 0.009	0.677 ± 0.019	0.606 ± 0.012
	BayesC	0.684 ± 0.018	0.586 ± 0.009	0.673 ± 0.019	0.607 ± 0.012
	deepGBLUP	0.712 ± 0.018	0.607 ± 0.009	0.702 ± 0.018	0.619 ± 0.011
2500	GBLUP	0.631 ± 0.016	0.515 ± 0.011	0.627 ± 0.025	0.539 ± 0.01
	DGBLUP	0.634 ± 0.016	0.514 ± 0.012	0.628 ± 0.024	0.539 ± 0.01
	EGBLUP	0.629 ± 0.016	0.514 ± 0.012	0.625 ± 0.025	0.538 ± 0.01
	BayesA	0.612 ± 0.016	0.500 ± 0.012	0.600 ± 0.022	0.525 ± 0.01
	BayesB	0.635 ± 0.015	0.515 ± 0.012	0.615 ± 0.025	0.531 ± 0.009
	BayesC	0.622 ± 0.016	0.508 ± 0.011	0.615 ± 0.025	0.534 ± 0.009
	deepGBLUP	0.660 ± 0.016	0.544 ± 0.013	0.650 ± 0.023	0.552 ± 0.01
1000	GBLUP	0.532 ± 0.017	0.384 ± 0.02	0.528 ± 0.018	0.424 ± 0.014
	DGBLUP	0.532 ± 0.017	0.381 ± 0.021	0.527 ± 0.018	0.424 ± 0.014
	EGBLUP	0.532 ± 0.017	0.384 ± 0.02	0.527 ± 0.018	0.423 ± 0.014
	BayesA	0.487 ± 0.018	0.361 ± 0.022	0.479 ± 0.018	0.404 ± 0.016
	BayesB	0.502 ± 0.015	0.365 ± 0.019	0.496 ± 0.019	0.405 ± 0.014
	BayesC	0.505 ± 0.015	0.365 ± 0.02	0.510 ± 0.018	0.402 ± 0.016
	deepGBLUP	0.557 ± 0.018	0.432 ± 0.018	0.564 ± 0.019	0.438 ± 0.013

Each value in the cells are means and standard errors of the predictive abilities for 10-fold tests. We highlight the best results in italic

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ISSN: 1297-9686

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